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<h1 class="title toc-ignore">2. Reading, Writing and Converting Simple
Features</h1>
<h4 class="author">Edzer Pebesma</h4>



<p><strong>For a better version of the sf vignettes see</strong> <a href="https://r-spatial.github.io/sf/articles/" class="uri">https://r-spatial.github.io/sf/articles/</a></p>
<p>This vignette describes how simple features can be read in R from
files or databases, and how they can be converted to other formats
(text, <a href="https://cran.r-project.org/package=sp">sp</a>)</p>
<div id="reading-and-writing-through-gdal" class="section level2">
<h2>Reading and writing through GDAL</h2>
<p>The Geospatial Data Abstraction Library (<a href="https://gdal.org/">GDAL</a>) is the swiss army knife for spatial
data: it reads and writes vector and raster data from and to practically
every file format, or database, of significance. Package <code>sf</code>
reads and writes using GDAL by the functions <code>st_read</code> and
<code>st_write</code>.</p>
<p>The data model GDAL uses needs</p>
<ul>
<li>a data source, which may be a file, directory, or database</li>
<li>a layer, which is a single geospatial dataset inside a file or
directory or e.g. a table in a database.</li>
<li>the specification of a driver (i.e., which format)</li>
<li>driver-specific reading or writing data sources, or layers</li>
</ul>
<p>This may sound complex, but it is needed to map to over 200 data
formats! Package <code>sf</code> tries hard to simplify this where
possible (e.g. a file contains a single layer), but this vignette will
try to point you to the options.</p>
<div id="using-st_read" class="section level3">
<h3>Using st_read</h3>
<p>As an example, we read the North Carolina counties SIDS dataset,
which comes shipped with the <code>sf</code> package by:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(sf)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>fname <span class="ot">&lt;-</span> <span class="fu">system.file</span>(<span class="st">&quot;shape/nc.shp&quot;</span>, <span class="at">package=</span><span class="st">&quot;sf&quot;</span>)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>fname</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;/tmp/RtmpoyoHrq/Rinstb81f123adcb20/sf/shape/nc.shp&quot;</span></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>nc <span class="ot">&lt;-</span> <span class="fu">st_read</span>(fname)</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## Reading layer `nc&#39; from data source </span></span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">##   `/tmp/RtmpoyoHrq/Rinstb81f123adcb20/sf/shape/nc.shp&#39; using driver `ESRI Shapefile&#39;</span></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## Simple feature collection with 100 features and 14 fields</span></span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry type: MULTIPOLYGON</span></span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="do">## Dimension:     XY</span></span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="do">## Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965</span></span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="do">## Geodetic CRS:  NAD27</span></span></code></pre></div>
<p>Typical users will use a file name with path for <code>fname</code>,
or first set R’s working directory with <code>setwd()</code> and use
file name without path.</p>
<p>We see here that a single argument is used to find both the
datasource and the layer. This works when the datasource contains a
single layer. In case the number of layers is zero (e.g. a database with
no tables), an error message is given. In case there are more layers
than one, the first layer is returned, but a message and a warning are
given:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="sc">&gt;</span> <span class="fu">st_read</span>(<span class="st">&quot;PG:dbname=postgis&quot;</span>)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>Multiple layers are present <span class="cf">in</span> data source PG<span class="sc">:</span>dbname<span class="ot">=</span>postgis, reading layer <span class="st">`</span><span class="at">meuse&#39;.</span></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="at">Use </span><span class="st">`</span>st_layers<span class="st">&#39; to list all layer names and their type in a data source.</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="st">Set the `layer&#39;</span> argument <span class="cf">in</span> <span class="st">`</span><span class="at">st_read&#39; to read a particular layer.</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="at">Reading layer </span><span class="st">`</span>meuse<span class="st">&#39; from data source `PG:dbname=postgis&#39;</span> using driver <span class="st">`</span><span class="at">PostgreSQL&#39;</span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="at">Simple feature collection with 155 features and 12 fields</span></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="at">geometry type:  POINT</span></span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a><span class="at">dimension:      XY</span></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="at">bbox:           xmin: 178605 ymin: 329714 xmax: 181390 ymax: 333611</span></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a><span class="at">epsg (SRID):    28992</span></span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a><span class="at">proj4string:    +proj=sterea +lat_0=52.15616055555555 ...</span></span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a><span class="at">Warning message:</span></span>
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a><span class="at">In eval(substitute(expr), envir, enclos) :</span></span>
<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a><span class="at">  automatically selected the first layer in a data source containing more than one.</span></span></code></pre></div>
<p>The message points to the <code>st_layers</code> command, which lists
the driver and layers in a datasource, e.g.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="sc">&gt;</span> <span class="fu">st_layers</span>(<span class="st">&quot;PG:dbname=postgis&quot;</span>)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>Driver<span class="sc">:</span> PostgreSQL </span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>Available layers<span class="sc">:</span></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>  layer_name geometry_type features fields</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="dv">1</span>      meuse         Point      <span class="dv">155</span>     <span class="dv">12</span></span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="dv">2</span>   meuse_sf         Point      <span class="dv">155</span>     <span class="dv">12</span></span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="dv">3</span>       sids Multi Polygon      <span class="dv">100</span>     <span class="dv">14</span></span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="dv">4</span>  meuse_tbl         Point      <span class="dv">155</span>     <span class="dv">13</span></span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a><span class="dv">5</span> meuse_tbl2         Point      <span class="dv">155</span>     <span class="dv">13</span></span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a><span class="sc">&gt;</span> </span></code></pre></div>
<p>A particular layer can now be read by e.g.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_read</span>(<span class="st">&quot;PG:dbname=postgis&quot;</span>, <span class="st">&quot;sids&quot;</span>)</span></code></pre></div>
<p><code>st_layers</code> has the option to count the number of features
in case these are missing: some datasources (e.g. OSM xml files) do not
report the number of features, but need to be completely read for this.
GDAL allows for more than one geometry column for a feature layer; these
are reported by <code>st_layers</code>.</p>
<p>In case a layer contains only geometries but no attributes (fields),
<code>st_read</code> still returns an <code>sf</code> object, with a
geometry column only.</p>
<p>We see that GDAL automatically detects the driver (file format) of
the datasource, by trying them all in turn.</p>
<p><code>st_read</code> follows the conventions of base R, similar to
how it reads tabular data into <code>data.frame</code>s. This means that
character data are read, by default as <code>factor</code>s. For those
who insist on retrieving character data as character vectors, the
argument <code>stringsAsFactors</code> can be set to
<code>FALSE</code>:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_read</span>(fname, <span class="at">stringsAsFactors =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<p>Alternatively, a user can set the global option
<code>stringsAsFactors</code>, and this will have the same effect:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">stringsAsFactors =</span> <span class="cn">FALSE</span>)</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="fu">st_read</span>(fname)</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Reading layer `nc&#39; from data source </span></span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="do">##   `/tmp/RtmpoyoHrq/Rinstb81f123adcb20/sf/shape/nc.shp&#39; using driver `ESRI Shapefile&#39;</span></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a><span class="do">## Simple feature collection with 100 features and 14 fields</span></span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry type: MULTIPOLYGON</span></span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a><span class="do">## Dimension:     XY</span></span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a><span class="do">## Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965</span></span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a><span class="do">## Geodetic CRS:  NAD27</span></span></code></pre></div>
</div>
<div id="using-st_write" class="section level3">
<h3>Using st_write</h3>
<p>To write a simple features object to a file, we need at least two
arguments, the object and a filename:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_write</span>(nc, <span class="st">&quot;nc1.shp&quot;</span>)</span></code></pre></div>
<p>The file name is taken as the data source name. The default for the
layer name is the basename (filename without path) of the the data
source name. For this, <code>st_write</code> needs to guess the driver.
The above command is, for instance, equivalent to:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_write</span>(nc, <span class="at">dsn =</span> <span class="st">&quot;nc1.shp&quot;</span>, <span class="at">layer =</span> <span class="st">&quot;nc.shp&quot;</span>, <span class="at">driver =</span> <span class="st">&quot;ESRI Shapefile&quot;</span>)</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Writing layer `nc&#39; to data source `nc1.shp&#39; using driver `ESRI Shapefile&#39;</span></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Writing 100 features with 14 fields and geometry type Multi Polygon.</span></span></code></pre></div>
<p>How the guessing of drivers works is explained in the next
section.</p>
</div>
<div id="guessing-a-driver-for-output" class="section level3">
<h3>Guessing a driver for output</h3>
<p>The output driver is guessed from the datasource name, either from
its extension (<code>.shp</code>: <code>ESRI Shapefile</code>), or its
prefix (<code>PG:</code>: <code>PostgreSQL</code>). The list of
extensions with corresponding driver (short driver name) is:</p>
<table>
<thead>
<tr class="header">
<th>extension</th>
<th>driver short name</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>bna</code></td>
<td><code>BNA</code></td>
</tr>
<tr class="even">
<td><code>csv</code></td>
<td><code>CSV</code></td>
</tr>
<tr class="odd">
<td><code>e00</code></td>
<td><code>AVCE00</code></td>
</tr>
<tr class="even">
<td><code>gdb</code></td>
<td><code>FileGDB</code></td>
</tr>
<tr class="odd">
<td><code>geojson</code></td>
<td><code>GeoJSON</code></td>
</tr>
<tr class="even">
<td><code>gml</code></td>
<td><code>GML</code></td>
</tr>
<tr class="odd">
<td><code>gmt</code></td>
<td><code>GMT</code></td>
</tr>
<tr class="even">
<td><code>gpkg</code></td>
<td><code>GPKG</code></td>
</tr>
<tr class="odd">
<td><code>gps</code></td>
<td><code>GPSBabel</code></td>
</tr>
<tr class="even">
<td><code>gtm</code></td>
<td><code>GPSTrackMaker</code></td>
</tr>
<tr class="odd">
<td><code>gxt</code></td>
<td><code>Geoconcept</code></td>
</tr>
<tr class="even">
<td><code>jml</code></td>
<td><code>JML</code></td>
</tr>
<tr class="odd">
<td><code>map</code></td>
<td><code>WAsP</code></td>
</tr>
<tr class="even">
<td><code>mdb</code></td>
<td><code>Geomedia</code></td>
</tr>
<tr class="odd">
<td><code>nc</code></td>
<td><code>netCDF</code></td>
</tr>
<tr class="even">
<td><code>ods</code></td>
<td><code>ODS</code></td>
</tr>
<tr class="odd">
<td><code>osm</code></td>
<td><code>OSM</code></td>
</tr>
<tr class="even">
<td><code>pbf</code></td>
<td><code>OSM</code></td>
</tr>
<tr class="odd">
<td><code>shp</code></td>
<td><code>ESRI Shapefile</code></td>
</tr>
<tr class="even">
<td><code>sqlite</code></td>
<td><code>SQLite</code></td>
</tr>
<tr class="odd">
<td><code>vdv</code></td>
<td><code>VDV</code></td>
</tr>
<tr class="even">
<td><code>xls</code></td>
<td><code>xls</code></td>
</tr>
<tr class="odd">
<td><code>xlsx</code></td>
<td><code>XLSX</code></td>
</tr>
</tbody>
</table>
<p>The list with prefixes is:</p>
<table>
<colgroup>
<col width="13%" />
<col width="86%" />
</colgroup>
<thead>
<tr class="header">
<th>prefix</th>
<th>driver short name</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>couchdb:</code></td>
<td><code>CouchDB</code></td>
</tr>
<tr class="even">
<td><code>DB2ODBC:</code></td>
<td><code>DB2ODBC</code></td>
</tr>
<tr class="odd">
<td><code>DODS:</code></td>
<td><code>DODS</code></td>
</tr>
<tr class="even">
<td><code>GFT:</code></td>
<td><code>GFT</code></td>
</tr>
<tr class="odd">
<td><code>MSSQL:</code></td>
<td><code>MSSQLSpatial</code></td>
</tr>
<tr class="even">
<td><code>MySQL:</code></td>
<td><code>MySQL</code></td>
</tr>
<tr class="odd">
<td><code>OCI:</code></td>
<td><code>OCI</code></td>
</tr>
<tr class="even">
<td><code>ODBC:</code></td>
<td><code>ODBC</code></td>
</tr>
<tr class="odd">
<td><code>PG:</code></td>
<td><code>PostgreSQL</code></td>
</tr>
<tr class="even">
<td><code>SDE:</code></td>
<td><code>SDE</code></td>
</tr>
</tbody>
</table>
</div>
<div id="dataset-and-layer-reading-or-creation-options" class="section level3">
<h3>Dataset and layer reading or creation options</h3>
<p>Various GDAL drivers have options that influences the reading or
writing process, for example what the driver should do when a table
already exists in a database: append records to the table or overwrite
it:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_write</span>(<span class="fu">st_as_sf</span>(meuse), <span class="st">&quot;PG:dbname=postgis&quot;</span>, <span class="st">&quot;meuse&quot;</span>, </span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>    <span class="at">layer_options =</span> <span class="st">&quot;OVERWRITE=true&quot;</span>)</span></code></pre></div>
<p>In case the table exists and the option is not specified, the driver
will give an error. Driver-specific options are documented in the driver
manual of <a href="https://gdal.org/drivers/vector/index.html">gdal</a>.
Multiple options can be given by multiple strings in
<code>options</code>.</p>
<p>For <code>st_read</code>, there is only <code>options</code>; for
<code>st_write</code>, one needs to distinguish between
<code>dataset_options</code> and <code>layer_options</code>, the first
related to opening a dataset, the second to creating layers in the
dataset.</p>
</div>
</div>
<div id="reading-and-writing-directly-to-and-from-spatial-databases" class="section level2">
<h2>Reading and writing directly to and from spatial databases</h2>
<p>Package <code>sf</code> supports reading and writing from and to
spatial databases using the <code>DBI</code> interface. So far, testing
has mainly be done with <code>PostGIS</code>, other databases might work
but may also need more work. An example of reading is:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(RPostgreSQL) </span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>conn <span class="ot">=</span> <span class="fu">dbConnect</span>(<span class="fu">PostgreSQL</span>(), <span class="at">dbname =</span> <span class="st">&quot;postgis&quot;</span>) </span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>meuse <span class="ot">=</span> <span class="fu">st_read</span>(conn, <span class="st">&quot;meuse&quot;</span>)</span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>meuse_1_3 <span class="ot">=</span> <span class="fu">st_read</span>(conn, <span class="at">query =</span> <span class="st">&quot;select * from meuse limit 3;&quot;</span>) </span>
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a><span class="fu">dbDisconnect</span>(conn) </span></code></pre></div>
<p>We see here that in the second example a query is given. This query
may contain spatial predicates, which could be a way to work through
massive spatial datasets in R without having to read them completely in
memory.</p>
<p>Similarly, tables can be written:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>conn <span class="ot">=</span> <span class="fu">dbConnect</span>(<span class="fu">PostgreSQL</span>(), <span class="at">dbname =</span> <span class="st">&quot;postgis&quot;</span>)</span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="fu">st_write</span>(conn, meuse, <span class="at">drop =</span> <span class="cn">TRUE</span>)</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a><span class="fu">dbDisconnect</span>(conn)</span></code></pre></div>
<p>Here, the default table (layer) name is taken from the object name
(<code>meuse</code>). Argument <code>drop</code> informs to drop
(remove) the table before writing; logical argument <code>binary</code>
determines whether to use well-known binary or well-known text when
writing the geometry (where well-known binary is faster and
lossless).</p>
</div>
<div id="conversion-to-other-formats-wkt-wkb-sp" class="section level2">
<h2>Conversion to other formats: WKT, WKB, sp</h2>
<div id="conversion-to-and-from-well-known-text" class="section level3">
<h3>Conversion to and from well-known text</h3>
<p>The usual form in which we see simple features printed is well-known
text:</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_point</span>(<span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>))</span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="do">## POINT (0 1)</span></span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a><span class="fu">st_linestring</span>(<span class="fu">matrix</span>(<span class="dv">0</span><span class="sc">:</span><span class="dv">9</span>,<span class="at">ncol=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>))</span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a><span class="do">## LINESTRING (0 1, 2 3, 4 5, 6 7, 8 9)</span></span></code></pre></div>
<p>We can create these well-known text strings explicitly using
<code>st_as_text</code>:</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">=</span> <span class="fu">st_linestring</span>(<span class="fu">matrix</span>(<span class="dv">0</span><span class="sc">:</span><span class="dv">9</span>,<span class="at">ncol=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>))</span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>str <span class="ot">=</span> <span class="fu">st_as_text</span>(x)</span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>x</span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a><span class="do">## LINESTRING (0 1, 2 3, 4 5, 6 7, 8 9)</span></span></code></pre></div>
<p>We can convert back from WKT by using <code>st_as_sfc</code>:</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_as_sfc</span>(str)</span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry set for 1 feature </span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry type: LINESTRING</span></span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a><span class="do">## Dimension:     XY</span></span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="do">## Bounding box:  xmin: 0 ymin: 1 xmax: 8 ymax: 9</span></span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a><span class="do">## CRS:           NA</span></span>
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a><span class="do">## LINESTRING (0 1, 2 3, 4 5, 6 7, 8 9)</span></span></code></pre></div>
</div>
<div id="conversion-to-and-from-well-known-binary" class="section level3">
<h3>Conversion to and from well-known binary</h3>
<p>Well-known binary is created from simple features by
<code>st_as_binary</code>:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">=</span> <span class="fu">st_linestring</span>(<span class="fu">matrix</span>(<span class="dv">0</span><span class="sc">:</span><span class="dv">9</span>,<span class="at">ncol=</span><span class="dv">2</span>,<span class="at">byrow=</span><span class="cn">TRUE</span>))</span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>(<span class="at">x =</span> <span class="fu">st_as_binary</span>(x))</span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="do">##  [1] 01 02 00 00 00 05 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 f0 3f</span></span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="do">## [26] 00 00 00 00 00 00 00 40 00 00 00 00 00 00 08 40 00 00 00 00 00 00 10 40 00</span></span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="do">## [51] 00 00 00 00 00 14 40 00 00 00 00 00 00 18 40 00 00 00 00 00 00 1c 40 00 00</span></span>
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a><span class="do">## [76] 00 00 00 00 20 40 00 00 00 00 00 00 22 40</span></span>
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a><span class="fu">class</span>(x)</span>
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;raw&quot;</span></span></code></pre></div>
<p>The object returned by <code>st_as_binary</code> is of class
<code>WKB</code> and is either a list with raw vectors, or a single raw
vector. These can be converted into a hexadecimal character vector using
<code>rawToHex</code>:</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="fu">rawToHex</span>(x)</span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="do">## [1] &quot;0102000000050000000000000000000000000000000000f03f000000000000004000000000000008400000000000001040000000000000144000000000000018400000000000001c4000000000000020400000000000002240&quot;</span></span></code></pre></div>
<p>Converting back to <code>sf</code> uses <code>st_as_sfc</code>:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">=</span> <span class="fu">st_as_binary</span>(<span class="fu">st_sfc</span>(<span class="fu">st_point</span>(<span class="dv">0</span><span class="sc">:</span><span class="dv">1</span>), <span class="fu">st_point</span>(<span class="dv">5</span><span class="sc">:</span><span class="dv">6</span>)))</span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="fu">st_as_sfc</span>(x)</span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry set for 2 features </span></span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="do">## Geometry type: POINT</span></span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a><span class="do">## Dimension:     XY</span></span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a><span class="do">## Bounding box:  xmin: 0 ymin: 1 xmax: 5 ymax: 6</span></span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a><span class="do">## CRS:           NA</span></span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a><span class="do">## POINT (0 1)</span></span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a><span class="do">## POINT (5 6)</span></span></code></pre></div>
</div>
<div id="conversion-to-and-from-sp" class="section level3">
<h3>Conversion to and from sp</h3>
<p>Spatial objects as maintained by package <code>sp</code> can be
converted into simple feature objects or geometries by
<code>st_as_sf</code> and <code>st_as_sfc</code>, respectively:</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="fu">methods</span>(st_as_sf)</span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="do">##  [1] st_as_sf.SpatVector*   st_as_sf.Spatial*      st_as_sf.data.frame*  </span></span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a><span class="do">##  [4] st_as_sf.lpp*          st_as_sf.map*          st_as_sf.owin*        </span></span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a><span class="do">##  [7] st_as_sf.ppp*          st_as_sf.ppplist*      st_as_sf.psp*         </span></span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a><span class="do">## [10] st_as_sf.s2_geography* st_as_sf.sf*           st_as_sf.sfc*         </span></span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a><span class="do">## [13] st_as_sf.wk_crc*       st_as_sf.wk_grd*       st_as_sf.wk_rct*      </span></span>
<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a><span class="do">## [16] st_as_sf.wk_wkb*       st_as_sf.wk_wkt*       st_as_sf.wk_xy*       </span></span>
<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a><span class="do">## see &#39;?methods&#39; for accessing help and source code</span></span>
<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a><span class="fu">methods</span>(st_as_sfc)</span>
<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a><span class="do">##  [1] st_as_sfc.SpatialLines*       st_as_sfc.SpatialMultiPoints*</span></span>
<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a><span class="do">##  [3] st_as_sfc.SpatialPixels*      st_as_sfc.SpatialPoints*     </span></span>
<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a><span class="do">##  [5] st_as_sfc.SpatialPolygons*    st_as_sfc.WKB*               </span></span>
<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a><span class="do">##  [7] st_as_sfc.bbox*               st_as_sfc.blob*              </span></span>
<span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a><span class="do">##  [9] st_as_sfc.character*          st_as_sfc.dimensions*        </span></span>
<span id="cb18-15"><a href="#cb18-15" aria-hidden="true" tabindex="-1"></a><span class="do">## [11] st_as_sfc.factor*             st_as_sfc.list*              </span></span>
<span id="cb18-16"><a href="#cb18-16" aria-hidden="true" tabindex="-1"></a><span class="do">## [13] st_as_sfc.map*                st_as_sfc.owin*              </span></span>
<span id="cb18-17"><a href="#cb18-17" aria-hidden="true" tabindex="-1"></a><span class="do">## [15] st_as_sfc.pq_geometry*        st_as_sfc.psp*               </span></span>
<span id="cb18-18"><a href="#cb18-18" aria-hidden="true" tabindex="-1"></a><span class="do">## [17] st_as_sfc.raw*                st_as_sfc.s2_geography*      </span></span>
<span id="cb18-19"><a href="#cb18-19" aria-hidden="true" tabindex="-1"></a><span class="do">## [19] st_as_sfc.sf*                 st_as_sfc.tess*              </span></span>
<span id="cb18-20"><a href="#cb18-20" aria-hidden="true" tabindex="-1"></a><span class="do">## [21] st_as_sfc.wk_crc*             st_as_sfc.wk_grd*            </span></span>
<span id="cb18-21"><a href="#cb18-21" aria-hidden="true" tabindex="-1"></a><span class="do">## [23] st_as_sfc.wk_rct*             st_as_sfc.wk_wkb*            </span></span>
<span id="cb18-22"><a href="#cb18-22" aria-hidden="true" tabindex="-1"></a><span class="do">## [25] st_as_sfc.wk_wkt*             st_as_sfc.wk_xy*             </span></span>
<span id="cb18-23"><a href="#cb18-23" aria-hidden="true" tabindex="-1"></a><span class="do">## see &#39;?methods&#39; for accessing help and source code</span></span></code></pre></div>
<p>An example would be:</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(sp)</span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(meuse)</span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a><span class="fu">coordinates</span>(meuse) <span class="ot">=</span> <span class="er">~</span>x<span class="sc">+</span>y</span>
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>m.sf <span class="ot">=</span> <span class="fu">st_as_sf</span>(meuse)</span>
<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a>opar <span class="ot">=</span> <span class="fu">par</span>(<span class="at">mar=</span><span class="fu">rep</span>(<span class="dv">0</span>,<span class="dv">4</span>))</span>
<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.sf)</span>
<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a><span class="do">## Warning: plotting the first 10 out of 12 attributes; use max.plot = 12 to plot</span></span>
<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a><span class="do">## all</span></span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>Conversion of simple feature objects of class <code>sf</code> or
<code>sfc</code> into corresponding <code>Spatial*</code> objects is
done using the <code>as</code> method, coercing to
<code>Spatial</code>:</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">=</span> <span class="fu">st_sfc</span>(<span class="fu">st_point</span>(<span class="fu">c</span>(<span class="dv">5</span>,<span class="dv">5</span>)), <span class="fu">st_point</span>(<span class="fu">c</span>(<span class="dv">6</span>,<span class="dv">9</span>)), <span class="at">crs =</span> <span class="dv">4326</span>)</span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">as</span>(x, <span class="st">&quot;Spatial&quot;</span>)</span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a><span class="do">## SpatialPoints:</span></span>
<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a><span class="do">##      coords.x1 coords.x2</span></span>
<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a><span class="do">## [1,]         5         5</span></span>
<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a><span class="do">## [2,]         6         9</span></span>
<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a><span class="do">## Coordinate Reference System (CRS) arguments: +proj=longlat +datum=WGS84</span></span>
<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a><span class="do">## +no_defs</span></span></code></pre></div>
</div>
</div>



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